End-to-End Mechanisms for Rate-Adaptive Multicast Streaming over the Internet

Language:

English

Abstract:

Continuous media applications over packet-switched networks are becoming more and more popular. Radio stations, for example, already use streaming technology to disseminate their content to users on the Internet, and video streaming services are expected to experience similar popularity. In contrast to traditional television and radio broadcast systems, however, prevalent Internet streaming solutions are based on unicast communication and raise scalability and efficiency issues. Multicast communication provides a promising and viable alternative since it can vastly improve scalability and network efficiency for the aforementioned class of applications. Nevertheless, suitable multicast streaming solutions ready for wide-area deployment are yet to emerge. In this thesis on Rate-Adaptive Multicast Streaming we provide mechanisms for improving multicast video streaming over the Internet. Our solutions address major issues that originate from the requirements for multicast solutions to scale to a large number of receivers and to accommodate the latter's heterogeneity of bandwidth capabilities. Therefore, our work exploits scalable-encoded video and utilizes layered multicast transmission on top of the IP multicast architecture. Choosing a modular design yields flexible techniques that can be integrated as building blocks in different transport and application frameworks. The proposed hybrid set of solutions includes mechanisms for server-side as well as receiver-driven rate adjustment. For the former purpose, we devise an algorithm that optimally stripes the scalable-encoded data into several media quality enhancing layers considering the distribution of receiver bandwidth capabilities. The underlying optimization metric is novel and incorporates transport as well as user aspects. It provides a mapping from each receiver's bandwidth capability onto a utility-based fairness measure. In order to provide means to the server for discovering the bandwidth capability distribution of the active receivers, we design a feedback scheme based on probabilistic polling. It allows to control the feedback traffic within statistical bounds, thus, making the scheme flexible and scaling to very large receiver populations. A key aspect in the design of scalable multicast solutions is the distribution of computational tasks and the reduction of control messages. Consequently, each receiver is responsible for inferring its bandwidth capability without involving the server. Therefore, we adopt and improve the state-of-the-art approach for estimating the fair bandwidth share based on TCP throughput modeling. Extensive simulation results prove the applicability of the modified scheme for estimating the TCP-fair rate of a multicast receiver. Thus, this information can be communicated to the source for rate optimization purposes utilizing our feedback scheme. In addition, it serves also for receiver-driven rate adaptation using a timer-based multicast group subscription strategy. Our novel approach yields a reasonable trade-off between the user demand for smooth video transmission and the network requirement of cooperativeness and responsiveness to congestion indication.